Onwunalu, 2006 - Google Patents
Optimization of nonconventional well placement using genetic algorithms and statistical proxyOnwunalu, 2006
View PDF- Document ID
- 10213272100837890755
- Author
- Onwunalu J
- Publication year
- Publication venue
- MS Report, Stanford University
External Links
Snippet
The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the …
- 238000005457 optimization 0 title abstract description 99
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Artus et al. | Optimization of nonconventional wells under uncertainty using statistical proxies | |
Zhao et al. | A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization | |
Amirian et al. | Performance forecasting for polymer flooding in heavy oil reservoirs | |
CN109416769B (en) | Computer-implemented method of generating an oil Field Development Plan (FDP) for producing hydrocarbon reservoirs | |
RU2496972C2 (en) | Device, method and system of stochastic investigation of formation at oil-field operations | |
US8005658B2 (en) | Automated field development planning of well and drainage locations | |
CN102362262B (en) | System and method for characterizing fractures in a subsurface reservoir | |
Guyaguler | Optimization of well placement and assessment of uncertainty | |
He et al. | Deep reinforcement learning for generalizable field development optimization | |
Foroud et al. | A comparative evaluation of global search algorithms in black box optimization of oil production: A case study on Brugge field | |
US20220164657A1 (en) | Deep reinforcement learning for field development planning optimization | |
Hutahaean et al. | Reservoir development optimization under uncertainty for infill well placement in brownfield redevelopment | |
Abdollahzadeh et al. | Bayesian optimization algorithm applied to uncertainty quantification | |
Han et al. | Production forecasting for shale gas well in transient flow using machine learning and decline curve analysis | |
Amirian et al. | Data-driven modeling approach for recovery performance prediction in SAGD operations | |
Wang et al. | Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty | |
CN114117881A (en) | Sand production risk prediction method and system | |
Onwunalu | Optimization of nonconventional well placement using genetic algorithms and statistical proxy | |
Hanea et al. | Drill and learn: a decision-making work flow to quantify value of learning | |
WO2017011469A1 (en) | Ensemble based decision making | |
Bukhamsin et al. | Optimization of multilateral well design and location in a real field using a continuous genetic algorithm | |
Sayyafzadeh | A self-adaptive surrogate-assisted evolutionary algorithm for well placement optimization problems | |
Tang et al. | Use of low-fidelity models with machine-learning error correction for well placement optimization | |
Ng et al. | Adaptive proxy-based robust production optimization with multilayer perceptron | |
Vaziri et al. | A machine learning‐based approach to the multiobjective optimization of CO2 injection and water production during CCS in a saline aquifer based on field data |